Data from: Home range and core area characteristics of urban and rural coyotes and red foxes in southern Wisconsin

Data from: Home range and core area characteristics of urban and rural coyotes and red foxes in southern Wisconsin Data files Nov 05, 2024 version files 325.91 KB - HomeRangeShapefiles.zip 322.70 KB - README.md 3.21 KB Abstract Second-order habitat selection is influenced by a variety of factors, including individual- and species-specific traits and resource requirements, as well as landscape characteristics. By comparing home range characteristics across individuals, species, and landscapes, we can draw conclusions regarding whether and how different factors influence home range selection. Our objectives were to quantify home range characteristics of VHF- and GPS-collared coyotes and red foxes in urban and rural areas of southern Wisconsin, including home range size and shape, home range stability, and inter- and intraspecific overlap. On average, urban coyotes had smaller home ranges with apparently greater intraspecific overlap between neighboring individuals than rural coyotes. Similarly, urban red foxes had smaller home ranges with apparently greater intraspecific overlap between neighboring individuals than urban coyotes. We found no difference in home range boundary complexity or stability between urban coyotes and red foxes or between urban and rural coyotes. We did identify greater interspecific overlap between urban coyotes and red foxes than has been previously reported. Our results provide further evidence that intrinsic and extrinsic factors, such as species characteristics, resource predictability, and availability as well as the physical environment, influence home range selection of coyotes and red foxes. https://doi.org/10.5061/dryad.d7wm37q9j Description of the data and file structure Our objectives were to quantify home range characteristics of VHF- and GPS-collared coyotes and red foxes in urban and rural areas of southern Wisconsin, including home range size and shape, home range stability, and inter- and intraspecific overlap. We captured and placed Very High Frequency (VHF) or Global Positioning System (GPS) collars on urban coyotes and red foxes beginning with a pilot study in 2014 as part of the University of Wisconsin Urban Canid Project (UCP). Location schedules varied with collar type (VHF or GPS). We fitted each individual with either a VHF radio collar (2014 and 2015 capture seasons; Advanced Telemetry Systems, Isanti, MN; Model M1950 for red fox and M2220B for coyote) or a Lotek LiteTrack Iridium GPS collar (2016 through 2022 capture seasons; Model #360 for coyotes and #150 for red foxes) For individuals with VHF collars, we located each radio-collared individual weekly using a five-hour bout, with a triangulated location recorded once every hour, during each bout, for as long as the VHF collar was active and the individual was alive. We rotated tracking bouts across the entire diel cycle to capture variation in temporal activity. For individuals with GPS collars, we programmed each collar to collect GPS fixes every hour only between 9pm and 4am for coyotes and 1am and 4am for red foxes. The Wisconsin Department of Natural Resources (WDNR) collected GPS data from coyotes captured in Iowa County as part of an independent research project on deer and their predators started in the fall of 2016. Staff fitted each animal with a Lotek LiteTrack GPS collar (Model #360) which they programmed to collect a GPS fix every three hours throughout the 24-hour diel cycle. All individuals were tracked until death or the end of the collar’s battery life. Files and variables File: HomeRangeShapefiles.zip Description: The HomeRangeShapefiles.zip contains a folder called HRShapefiles which holds two sets of shapefiles. The AllKDE shapefile contains all individual 95% fixed kernel density home range estimators for coyotes and red foxes included in this manuscript. The AllMCP shapefile contains all individual 95% minimum convex polygon home ranges for coyotes and red foxes included in this manuscript. Variables in the attribute table are as follows: - FID: a unique row ID appended by ArcPro - Shape: the vector type - level: specifies the level of the home range estimator (e.g., 0.95 indicates 95%) - what: identifies rows as estimates of home ranges - area: area in square kilometers of the home range polygon - id: number used to identify individual animals - n: number of locations used in the home range estimation - Perimeter: distance of the polygon boundary in kilometers - PARatio: the perimeter area ratio Code/software The data is in a shapefile format used by ArcPro and other ArcGIS software. GIS software is needed to view or convert the files. QGIS is a free alternative. Data collection – Urban study area We captured and placed Very High Frequency (VHF) or Global Positioning System (GPS) collars on urban coyotes and red foxes beginning with a pilot study in 2014 as part of the University of Wisconsin Urban Canid Project (UCP). We captured coyotes and red foxes in our urban study area annually between October and March using cable restraints while following trapping best management practices. We selected trap sites based on landscape characteristics such as greenspace and sightings reported to the project’s iNaturalist page (“UW Urban Canid Project iNaturalist Project”), and we used bait including carcasses of road-killed deer for coyotes and nuisance-trapped beavers for red foxes to attract canids to a site, especially when there was minimal snow cover. We chemically immobilized captured canids with an intramuscular injection of 4-10 mg/kg ketamine and 2-4 mg/kg xylazine based on estimated weight of the individual. We then determined weight and physical condition, collected biological samples, and fitted the animal with either a VHF radio collar (2014 and 2015 capture seasons; Advanced Telemetry Systems, Isanti, MN; Model M1950 for red fox and M2220B for coyote) or a Lotek LiteTrack Iridium GPS collar (2016 through 2022 capture seasons; Model #360 for coyotes and #150 for red foxes). After handling, we reversed the immobilization with an intramuscular injection of either yohimbine (0.1 mg/kg) or antisedan (0.6-0.7 mg/kg). We then released the animal at the capture site. Location schedules varied with collar type (VHF or GPS). For individuals with VHF collars, we located each radio-collared individual weekly using a five-hour bout, with a triangulated location recorded once every hour, during each bout, for as long as the VHF collar was active and the individual was alive. We rotated tracking bouts across the entire diel cycle to capture variation in temporal activity. For individuals with GPS collars, we programmed each collar to collect GPS fixes every hour only between 9pm and 4am for coyotes and 1am and 4am for red foxes. Species-specific time periods were selected to maximize locations during periods of high activity in urban areas and optimize battery life of the telemetry collars. Individuals were tracked until death or the end of the collar’s battery life. Data collection – Rural study area The Wisconsin Department of Natural Resources (WDNR) collected GPS data from coyotes captured in Iowa County as part of an independent research project on deer and their predators. The WDNR started this project in fall of 2016. Coyotes were captured using cable restraints or foothold traps either through collaboration with trappers and landowners who would voluntarily report a captured coyote or through traps set by WDNR staff. Captured coyotes were anesthetized via an injection of ketamine-dexmedetomidine-butorphanol (4 mg/kg ketamine, 0.2 mg/kg dexmedetomidine, and 0.4 mg/kg butorphanol) based on estimated weight. Staff then weighed and fitted each animal with a Lotek LiteTrack GPS collar (Model #360) which they programmed to collect a GPS fix every three hours throughout the 24-hour diel cycle. Individuals were tracked until death or the end of the collar’s battery life. Home range delineation Using radio-location data from both study sites, we calculated home range (95%) size for individual coyotes and red foxes using Minimum convex polygons (MCP) and Fixed kernel density estimators (KDE) with the amt R package. We chose to retain all locations for each individual rather than thinning data to standardize the tracking schedules between urban and rural individuals and VHF-collared and GPS-collared individuals. There were several individuals in the dataset with relatively few location points (n ≤ 50), so we used area-observation curves to determine whether each individual had sufficient data to reach an asymptote, and excluded individuals with too few locations. Similarly, to ensure that dispersing individuals and transients did not artificially inflate average home range sizes, we excluded individuals with large data sets whose home range area also failed to reach a stable asymptote as determined by area-observation curves.